1. Field of the Invention
The present invention is related to an image processing apparatus and method and, more particularly, to an image processing apparatus and method for noise suppression.
2. Description of the Prior Art
In a digital imaging system such as digital camera or digital video camera, raw images of an object/scene captured from a sensing or capture device are often subject to various types of “noise” (elements not present in the object or environment which may nonetheless appear in the image). The presence of noise in an image is perhaps caused by the characteristics of the imaging system, such as the sensor, or processing steps subsequent to the initial image capture, which may add noise while trying to achieve a different purpose. The properties and characteristics that would identify a pixel or a region of pixels as “noisy” and the properties that would identify a pixel or a region of pixels as an edge or a fine detail of the image are difficult to distinguish. Thus, the noise inhibiting methods of the prior arts often remove the edge or detail pixels or region of pixels of the image, and therefore a blurring effect occurs within that region of the image, and lowers the quality of the image. In addition, in color images, the blurring effect leads to a bleeding of one color across the edge to another pixel(s).
In the prior art, when the object/scene is imaged by a sensing or imaging device, such as a digital camera, the resultant image in captured into a CFA (Color Filter Array) bearing a particular color channel pattern. One oft-used pattern for capturing images is known as the Bayer pattern, which has color channels as follows,G R G R G R G . . .B G B G B G B . . .G R G R G R G . . .
Thus, in a Bayer pattern CFA, each pixel location has an intensity value associated only with one of the three color planes (Green, Red and Blue) which combine to make a full color. The process of estimating the two missing color components for each pixel location is known in the art as color interpolation. The interpolation of color often precedes the removal of noise in color mages due to the fact that most traditional noise reduction or removal techniques are designed to operate upon images with full color pixel information. The process of color interpolation itself will introduce noises, such that the original captured image noise may be blended with other noises and may perhaps lose the distinction of being noises and gain the distinction of being an image feature.
Traditionally, performing noise removal on the full color pixels attained by the color interpolation process increases the memory and processing needs of the noise removal process by three times (since each pixel has thrice the resolution), and thus it is difficult and expensive to improve the noise removal in hardware. Other noise removal techniques attempt to reduce this burden by performing color space conversion after color interpolation into, for instance, the YUV space, where only the Y (chrominance) component is considered for noise removal, so as to reduce the burden of hardware. However, this too may propagate additional noise beyond that propagated by color interpolation and cannot be easily implemented in hardware as well.
Thus, there is a need for a noise reduction framework that will not only distinguish edge pixels from non-edge pixels, but also one that can work directly in the CFA image domain prior to any color interpolation, so as to increase the processing efficiency, and lower the hardware cost.